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7. AI System Safety, Failures, & Limitations2 - Post-deployment

Nuclear Power Systems

General-purpose AI deployed for reactor monitoring, control system optimization, or emergency response coordination could misinterpret sensor data, fail to recognize critical safety conditions, or make erroneous control decisions during emergency scenarios. Given the catastrophic potential of nuclear accidents, even minor AI reasoning errors in safety-critical functions could lead to core meltdowns, radiation releases, or widespread contamination affecting hundreds of thousands of people across international borders.

Source: MIT AI Risk Repositorymit1453

ENTITY

2 - AI

INTENT

2 - Unintentional

TIMING

2 - Post-deployment

Risk ID

mit1453

Domain lineage

7. AI System Safety, Failures, & Limitations

375 mapped risks

7.3 > Lack of capability or robustness

Mitigation strategy

1. Implement a Human-in-the-Loop Architecture with Assured Graceful Degradation. All AI-generated control or emergency response decisions must be explicitly validated by human operators, with the system architected for controlled disengagement (fail-safe mode) and immediate reversion to manual control or redundant conventional systems upon detection of high-confidence uncertainty, anomalous input data, or a failure to meet real-time performance and safety constraints. 2. Establish a Rigorous, Continuous AI Assurance and Safety Monitoring Framework. This framework must mandate real-time, post-deployment monitoring of the AI model's performance to detect and automatically flag instances of concept drift or data drift (shifts in operational parameters or sensor data patterns), ensuring the model is retired or retrained before its predictive reliability degrades below specified safety thresholds. 3. Integrate Explainable AI (XAI) and Formal Methods with Domain Knowledge. Prioritize AI models whose decision-making processes are transparent and auditable (explainability). Where 'black-box' models are unavoidable, their outputs must be constrained and mathematically validated using formal methods and constraint-based learning to guarantee adherence to fundamental nuclear physics and engineering safety principles (e.g., thermal-hydraulic limits).